Mode Selection For Modal Reverb

Abstract

Methods and systems for performing modal reverb techniques for audio signals are described. The method may involve simplifying a reverb effect to be applied to the audio signal by receiving an IR, dividing the IR into a plurality of sub-bands, using a parametric estimation algorithm to determine respective parameters of the modes included in each sub-band, aggregating the respective modes of the sub-bands into a set; and truncating the set of aggregated modes into a subset of modes. Reverberation of the audio signal may be manipulated based on an IR that itself is based on the truncated subset of modes.

Description

BACKGROUND[0001]Audio engineers, musicians, and even the general population (collectively “users”) are accustomed to generating and manipulating audio signals. For instance, audio engineers edit stereo signals by mixing together monophonic audio signals using effects such as pan and gain to position them within the stereo field. Users also manipulate audio signals into individual components for effects processing using multiband structures, such as crossover networks, for multiband processing. Additionally, musicians and audio engineers regularly use audio effects, such as compression, distortion, delay, reverberation, etc., to create sonically pleasing, and in some cases unpleasant sounds. Audio signal manipulation is typically performed using specialized software or hardware. The type of hardware and software used to manipulate the audio signal is generally dependent upon the user's intentions. Users are constantly looking for new ways to create and manipulate audio signals.[0002]...

AI Technical Summary

Benefits of technology

The patent text describes an improved technique for analyzing the sound in a room by using a recording of an impulse response. The technique uses a method called convolutional reverb, which splits the recording into different frequency bands and estimates the parameters of each band using a statistical technique called SVD. The modes of the room are represented by these parameters, and this high-resolution analysis allows for better discrimination between them. The text also suggests dividing the recording into sub-bands to further improve the resolution of the analysis. Overall, this technique provides a better understanding of the sound in a room and can aid in designing better room acoustics.

Problems solved by technology

Additionally, musicians and audio engineers regularly use audio effects, such as compression, distortion, delay, reverberation, etc., to create sonically pleasing, and in some cases unpleasant sounds.

However, the techniques for manipulating the parameters of a convolutional reverb are relatively limited.

For instance, using convolutional reverb, it may not be possible to isolate and manipulate the resonance of a single frequency within the audio signal.

Additionally, using convolutional reverb, it also may not be possible to adjust or manipulate a single property of a simulated physical space (e.g., the space's length, the space's width).

One drawback of currently known modal reverb techniques is the degree of processing required.

The amount of required processing can be reduced by dropping modes from the audio signal, but this has the unwanted effect of reducing quality of the audio signal.

Another drawback of modal reverb techniques is that it is difficult to identify all of the modes in an acoustic space.

Previous techniques do not provide a high enough resolution to properly identify all of the modes.

However, DFT-based mode identification has a low resolution.

As a result of the low resolution, the simulated physical space can only be approximated, and cannot easily be scaled.

Altogether, the DFT-based modal reverb technique may provide some manipulability of an audio signal, but with degraded quality, and with inaccurate scalability.

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